Abstract
Abstract. This study takes Guangxi Huixian National Wetland Park as the research area, and uses the UAV image and ground measured tag data as the data source. The SegNet model is used to extract the wetland vegetation information in the study area, further verification multiple classification SegNet model and fusion multiple SegNet model of single/double classification precision of the two ways of extracting karst wetland vegetation information. The experimental results show that the Kappa coefficient of the multi-segmented SegNet model is 0.68, while the multi-class SegNet model has a classification effect of 0.59. The classification effect of the karst wetland vegetation information extracted by multiple single/double-class SegNet models is more than the multi-classification. The SegNet model has high precision.
Highlights
At present, the extraction of wetland vegetation information is mainly based on machine learning algorithm
The current machine learning algorithm has good precision for wetland vegetation information extraction, but the deep learning algorithm is rarely used in this field
Deep learning algorithm is mainly applied to the extraction of single category of architectural information based on spaceborne optical images with an accuracy of more than 80%, while deep learning algorithm based on multi-category information extraction of low-altitude man-machine image wetlands is rarely studied
Summary
The extraction of wetland vegetation information is mainly based on machine learning algorithm. Among the machine learning algorithm, random forest algorithm [1] and decision tree [2,3] algorithm have good effects on the extraction of wetland vegetation information. The sinkhole-extraction random forest was grown on a training dataset built from an area where LiDARderived depressions were manually classified through a visual inspection and field verification process. The weighted random forest achieved an average accuracy of 89.95% for the training dataset, demonstrating that the random forest can be an effective sinkhole classifier. The current machine learning algorithm has good precision for wetland vegetation information extraction, but the deep learning algorithm is rarely used in this field. Deep learning algorithm is mainly applied to the extraction of single category of architectural information based on spaceborne optical images with an accuracy of more than 80%, while deep learning algorithm based on multi-category information extraction of low-altitude man-machine image wetlands is rarely studied
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